An assessment of pasture soils quality based on multi-indicator weighting approaches in semi-arid ecosystem

• Published in: Ecological indicators Vol. 121; p. 107001
• Main Authors: Karaca, Siyami, Dengiz, Orhan, Demirağ Turan, İnci, Özkan, Barış, Dedeoğlu, Mert, Gülser, Füsun, Sargin, Bulut, Demirkaya, Salih, Ay, Abdurahman
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-02-2021; Elsevier
• Subjects: Fuzzy-AHP; Pasture; Principal component analysis; REOSAVI; Semi-arid ecosystem; Soil quality; REOSAVI; Soil quality; Fuzzy-AHP; Pasture; Semi-arid ecosystem; Principal component analysis
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2020.107001

•Development of the soil quality index is important for strategical areas.•Use of TDSSQI/MDSSQI integration with GIS to develop a pastureland evaluation model.•Testing the success of SQI with biomass reflection.•To help the planning of pastureland by using several indicators for decision makers.
The development of soil quality index in the vicinity of the Van Lake pasture lands located in the Northern East Part of Turkey under semi-arid terrestrial ecosystem is very important since there are certain degradation signs indicating how their sustainability is being threatened. A total of 150 soils in the pastures throughout the region were sampled and several soil physical, chemical and biological indicators were quantified. A minimum data set of the most sensitive indicators was chosen using principal component analyses. Linear scoring functions for these indicators were used to develop soil quality index integrated with remote sensing (RS) and geographical information system (GIS). In this current study, classes between SQIs calculated using the minimum data set (MDS) and total data set (TDS) approaches showed a parallel trend in each other and match analysis for agreement showed also a significant statistically relationship between TDSSQI/MDSSQI and REOSAVI in May and June months for pasture area. Furthermore, this study also showed that advance techniques (PCA, geostatistic, AHP-Fuzzy) and the technologies of RS and GIS, which are essential to the analysis and processing of original and generated information were used effectively by integrating each other for SQI in large area.